Zhehang Tong

National University of Defense Technology

Papers

Total Citations

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About

Zhehang Tong is a researcher whose work bridges scene understanding and robotic perception, with a focus on making autonomous systems more adaptive to real-world environments. His key research areas include indoor-outdoor scene classification and robust simultaneous localization and mapping (SLAM). Tong made a significant contribution by proposing SceneSLAM, a novel extensible framework that integrates scene detection into SLAM systems to enhance robustness. This work addresses a critical challenge: different sensors may fail in different scenes, and Tong's approach allows SLAM to dynamically adapt, improving accuracy and reliability. His most cited paper, "A Review of Indoor-Outdoor Scene Classification" (2017, 14 citations), provides a comprehensive survey of a problem that has been studied for nearly two decades, highlighting its applications in image retrieval, robot navigation, and general scene classification. In "Dynamic Adaptive Simultaneous Localization and Mapping Technique for Scene Change" (2018, 2 citations), he further advanced the field by tackling how SLAM systems can maintain reliability in complex, changing environments. Tong's work is particularly notable for its practical focus on making robots more autonomous and dependable in real-world scenarios, a crucial step toward widespread deployment of intelligent systems.